During your thesis, you design, implement, and validate an innovative predictive energy management algorithm for advanced Vehicle-to-Grid/Home (V2X) applications.
Furthermore, you optimize daily energy flows to ensure maximum efficiency while proactively minimizing the degradation of vehicle batteries.
You resolve the complex trade-off between short-term economic gains as well as the long-term preservation of battery State of Health (SoH).
In addition, you select appropriate battery aging models—such as SPMe, ECM, or simplified PyBaMM models—and integrate them seamlessly into the optimization process.
Lastly, you validate your models using a data-driven approach, leveraging both publicly available and confidential internal datasets.
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